Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment (BSFA-FSFG)

Authors: Zican Zha, Hao Tang, Yunlian Sun, and Jinhui Tang.

This repository provides code for "Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment" IEEE TCSVT 2023.

Requirements

Python 3.6
Pytorch >= 1.7.0
Torchvision = 0.10
scikit-image = 0.18.1

Data Preparation

Download Datasets from Baidu Drive (extraction code: ZZC3)
Download Datasets from Google Drive

How to run


python train.py --dataset [type of dataset] --model [backbone] --num_classes [num-classes] --nExemplars [num-shots]
python test.py --dataset CUB-200-2011 --model R --num_classes 100 --nExemplars 5

# Example: run on CUB dataset, ResNet-12 backbone, 5-way 1-shot
python train.py --dataset CUB-200-2011 --model R --num_classes 100 --nExemplars 1
python test.py --dataset CUB-200-2011 --model R --num_classes 100 --nExemplars 1

Citation

Please cite our paper if you find the work useful, thanks!

     @article{zha2023boosting,
       title={Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment},
       author={Zha, Zican and Tang, Hao and Sun, Yunlian and Tang, Jinhui},
       journal={IEEE Transactions on Circuits and Systems for Video Technology},
       year={2023},
       publisher={IEEE},
       doi={10.1109/TCSVT.2023.3236636}
    }

Other related papers

     @article{TangYLT22,
          author    = {Hao Tang and Chengcheng Yuan and Zechao Li and Jinhui Tang},
          title     = {Learning attention-guided pyramidal features for few-shot fine-grained recognition},
          journal   = {Pattern Recognit.},
          volume    = {130},
          pages     = {108792},
          year      = {2022}
     }

Acknowledgement

This code is based on the implementations of fewshot-CAN.

CSer-Tang-hao / BSFA-FSFG

readme